Content Readability Optimizer
Analyzes and optimizes content readability using textstat Python library and Hemingway API patterns. Computes Flesch-Kincaid, Gunning Fog, and SMOG indices with automated rewriting suggestions.
npx skills add agentskillexchange/skills --skill content-readability-optimizer
The Content Readability Optimizer provides comprehensive text analysis and improvement recommendations using the textstat Python library for readability scoring and NLP-based rewriting suggestions. It evaluates content across multiple readability indices simultaneously.
What this skill actually does
Core metrics include Flesch-Kincaid Grade Level, Gunning Fog Index, SMOG Index, Coleman-Liau Index, and Automated Readability Index. The agent identifies specific passages that reduce readability scores, highlighting complex sentences, passive voice usage, adverb density, and jargon patterns that impede comprehension.
Advanced features include audience-targeted optimization where content is adjusted to match specific grade levels, A/B variant generation for different audience segments, and batch processing for large content libraries. The agent integrates with CMS APIs to pull content directly, analyze it, and push optimized versions back. It also generates readability trend reports over time and supports custom vocabulary allowlists for technical domains.